Radiometric Preprocessing for Atmospheric Correction
Learn about radiometric preprocessing techniques for atmospheric correction in remote sensing imagery. Explore the effects of sun angle differences, atmospheric scattering, and absorption on spectral-radiometric responses. Discover methods such as histogram adjustment and dark object subtraction for enhancing image quality.
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Presentation Transcript
Radiometric Preprocessing: Atmospheric Correction
Radiometric Preprocessing Correction for Sun Angle Differences l is the solar zenith angle and varies as a function of latitude, day of year, and time of day Sun Ground l Radiance is proportional to cos . Therefore, we can adjust for sun angle effects by: n adjusted DN = (cos / cos ) x DN u where = input sun angle and is the normalized or reference angle (e.g., 45 degrees or for one of several dates of imagery
Effects of atmospheric scattering and absorption on spectral-radiometric responses l Effects of scattering on image quality n Reduces contrast dark objects are brighter, bright objects are darker l Visible bands are much more subject to atmospheric effects than infrared bands n Sensitivity of spectral bands (high to low) to atmospheric effects u blue, green, red, near IR, middle IR
Methods for Atmospheric Correction l Atmospheric (radiative transfer) models n Most rigorous, best, but complex n Difficult to obtain necessary input data Example of atmospheric correction with the ATCOR model Before After Jensen, 2007
Histogram adjustment n Shifts histograms to left (to zero) under the assumption that very dark objects (0% reflectance) would be at zero if it were not for the added response due to atmospheric scattering u i.e., the path radiance term in the remote sensing equation n Assumes the magnitude of scattering is the same for all cover types (not exactly correct) n Does not correct for atmospheric absorption effects l
Histogram Adjustment (Dark Object Subtraction) Find pixels of dark objects that are assumed to have near zero reflectance n Deep, clear lakes are good Subtract their DN value from all pixels (e.g., 30) n Has the effect of shifting the histogram to start at 0 l Number of Pixels Shift attributed to haze l 0 30 255 Digital Number
Histogram Adjustment (Dark Object Subtraction) Find pixels of dark objects that are assumed to have near zero reflectance n Deep, clear lakes are good Subtract their DN value from all pixels (e.g., 30) n Has the effect of shifting the histogram to start at 0 l New histogram Number of Pixels l 0 30 255 Digital Number
Demonstration of Image Enhancement with Image Processing Software l Spectral band combinations l Radiometric Enhancement: Contrast stretch l Spatial Enhancement: Convolution filtering l Principal Components
